Genetic Algorithm Search for Predictive Patterns in Multidimensional Time Series

Abstract

Based on an algorithm for pattern matching in character strings, a pattern matching machine is implemented that searches for occurrences of patterns in multidimensional time series. Before the search process takes place, time series data is encoded in user-designed alphabets. The patterns, on the other hand, are formulated as regular expressions that are composed of letters from these alphabets and operators. Furthermore, a genetic algorithm is developed to breed patterns that maximize a userdefined fitness function. In an application to financial data, it is shown that patterns bred to predict high exchange rates volatility in training samples retain statistically significant predictive power in validation samples.